In-situ Estimation of Time-averaging Uncertainties in Turbulent Flow Simulations

TL;DR

This paper introduces an in-situ framework for real-time estimation of uncertainties in turbulence statistics during simulations, overcoming offline limitations and enabling efficient, accurate uncertainty quantification in large-scale turbulent flow computations.

Contribution

The authors develop a novel low-memory update algorithm for autocorrelation functions, allowing online uncertainty estimation in turbulent flow simulations without significant computational overhead.

Findings

The in-situ method provides uncertainty estimates comparable to offline methods.

The framework is computationally efficient and adaptable to various flow solvers.

It is applicable to complex meshes and adaptive refinement techniques.

Abstract

The statistics obtained from turbulent flow simulations are generally uncertain due to finite time averaging. The techniques available in the literature to accurately estimate these uncertainties typically only work in an offline mode, that is, they require access to all available samples of a time series at once. In addition to the impossibility of online monitoring of uncertainties during the course of simulations, such an offline approach can lead to input/output (I/O) deficiencies and large storage/memory requirements, which can be problematic for large-scale simulations of turbulent flows. Here, we designed, implemented and tested a framework for estimating time-averaging uncertainties in turbulence statistics in an in-situ (online/streaming/updating) manner. The proposed algorithm relies on a novel low-memory update formula for computing the sample-estimated autocorrelation functions (ACFs). Based on this, smooth modeled ACFs of turbulence quantities can be generated to accurately estimate the time-averaging uncertainties in the corresponding sample mean estimators. The resulting uncertainty estimates are highly robust, accurate, and quantitatively the same as those obtained by standard offline estimators. Moreover, the computational overhead added by the in-situ algorithm is found to be negligible. The framework is completely general and can be used with any flow solver and also integrated into the simulations over conformal and complex meshes created by adopting adaptive mesh refinement techniques. The results of the study are encouraging for the further development of the in-situ framework for other uncertainty quantification and data-driven analyses relevant not only to large-scale turbulent flow simulations, but also to the simulation of other dynamical systems leading to time-varying quantities with autocorrelated samples.